Military cybersecurity faces increasing threats from Advanced Persistent Threats (APTs), zero-day exploits, and adversarial AI-driven malware, necessitating real-time, adaptive defense mechanisms to protect critical networks, UAV systems, and cyber-physical infrastructures. Traditional detection methods, such as signature-based approaches, struggle with high false positives and poor zero-day attack detection, making them inadequate in addressing evolving cyber threats. Unlike traditional methods, AI-driven approaches, particularly deep learning, provide significant improvements in malware classification accuracy, real-time detection, and robustness against adversarial attacks. This research introduces a deep learning-based malware detection framework that integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Transformer models, and Reinforcement Learning (RL) to enhance malware detection performance. The framework employs a hybrid AI-powered threat intelligence system that combines static, dynamic, and adversarial AI-based defenses to counter evolving malware tactics such as polymorphism, obfuscation, and zero-day exploits. Experimental results demonstrate over 99% detection accuracy, 99.5% adversarial robustness, and inference speeds under 15ms, ensure low-latency threat response for 5G/6G tactical networks and military cyber defense systems. By incorporating GAN-based adversarial training and integrating real-time cyber threat intelligence (CTI) platforms, this research advances next-generation AI-driven military cybersecurity solutions, enhancing resilience, adaptability, and autonomous defense against modern cyber warfare threats. Unlike traditional methods, this approach delivers robust, scalable, and adaptive defense mechanisms, critical for securing military assets in the face of advanced cyber warfare tactics.